Department of Earth Sciences; IIT Bombay, Mumbai

mdanees20@gmail.com

10th Biennial International Conference & Exposition

P 119

Seismic Attribute Analysis for Reservoir Characterization

Mohammad Anees*

Summary

Seismic attributes can be important qualitative and quantitative predictors of reservoir properties and geometries when

correctly used in reservoir characterization studies. Seismic attributes reveal information, which are not readily apparent in

the raw seismic data. While the ultimate goal of reservoir characterization is to identify reservoir, delineate the pay zone

and determine the distribution of their relevant properties such as thickness, porosity and lithology.

This paper discusses the seismic attribute and reservoir characterization analysis for the data from F3 block in the Dutch

sector of the North Sea. Instantaneous amplitude attribute is computed which confirms the bright spot due to the presence of

gas in the reservoir. Next, the thickness information is estimated from the spectral decomposition. To determine the porosity

in the reservoir zone, the cross plot analysis is carried out using the well log data. The porosity log data derived from the

well log with different seismic attributes was plotted to establish a linear regression relationship which is then used to get

the porosity. From the seismic attribute studies and well log analysis the estimated thickness of the reservoir zone is about

12.2 m and the porosity in the reservoir zone varies from 28-32 %.

Keywords: Seismic attribute, Reservoir characterization, Thickness and Porosity

Introduction

Seismic attributes have been increasingly used in both

exploration and reservoir characterization studies and

routinely been integrated in the seismic interpretation

processes (Partyka et al., 1999). There are different

classes of seismic attributes based upon the nature of

estimation and property of the reservoir they reveal. For

estimation of seismic attributes any of the following can

be used as input: a single seismic trace, a set of pre-stack

CMP or CRP gathers or the entire seismic volume

(Partyka, 2001; Liu and Marfurt, 2006).

The principle objective of the attributes analysis is to

provide accurate and detailed information to the

interpreter on structural, stratigraphic and lithological

parameters of the reservoir. In our current study, the

attribute analysis is estimated to characterize the reservoir

in terms of porosity and thickness of the hydrocarbon

bearing zone. The instantaneous amplitude attribute is

calculated to confirm the presence of bright spot

suggestive of presence of gas in the reservoir zone. Next,

the cross plot and spectral decomposition analysis is

performed to estimate the porosity and thickness of the

reservoir zone. For the cross plot analysis, the porosity

data is provided by dGBEarthSciences from the well F-

304 present in the survey area F3 block in the Dutch

sector of the North Sea.

Methodology

There are different techniques for estimation of different

seismic attributes. The instantaneous amplitude attribute

to locate the reservoir (gas bearing zone) is calculated.

This is achieved through complex trace attribute analysis

as explained by Taner et al. (1979). In this method, a

seismic trace is considered as a complex trace having real

and quadrature component. Real part is the actual seismic

trace recorded.

Next, the thickness is estimated following the spectral

decomposition analysis in the reservoir zone to get the

dominant frequency in that zone. The studies performed

by Partyka et al. (1999), Partyka(2001), and Liu and

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Marfurt(2006) demonstrate the effectiveness of spectral

decomposition using the discrete Fourier transform

(DFT) as a thickness estimation tool. In thickness

mapping there is an inverse relation between the

dominant frequency and the thickness of the target zone.

Dominant frequency characterizes the thickness of the

bed and the amplitude is known to be maximum at the

tuning thickness estimated from the dominant frequency

(Partyka et al., 1999). Thus, spectral decomposition can

reveal and map seismic features as a function of spatial

position, travel time, frequency, amplitude and phase and

help us to visualize, interpret and quantify the seismic

response to an extent that was previously unattainable

(Partyka et al., 1999).

Fig 1: Seismic section containing the reservoir zone along inline

228. The reservoir is calculated to be at the depth of about 520

ms and is highlighted with a yellow rectangle. Horizon H1 is

marked with green color.

For Porosity estimation the crossplot analysis was

performed using well data located in the survey area. The

porosity log data derived from the well log with different

seismic attributes was plotted to establish a linear

regression relationship. This regression relation was used

throughout the survey area to get the porosity in the

reservoir zone.

Case Study

The case study analyses the post-stack time migrated 3D

seismic data provided by dGBEarthSciences through

Opendtect share seismic data repository. Data is

originally collected from the F3 block in the Dutch sector

of the North Sea. The seismic data is accompanied with

only one well F-304 data in the region. A horizon H1

along the seismic inline 228 is picked for the analysis

(Fig-1). The section contains a bright spot at about 520ms

possibly due to the presence of biogenic gas packet.

Bright spot is clearly visible in the seismic section which

is indicated with a yellow rectangle. The instantaneous

attribute is computed using Opendtect software which

verifies this bright spot in the section. Fig-2 shows the

instantaneous amplitude attribute along the inline 228 and

the horizon H1 passing through the bright spot zone.

Both the above attributes clearly show the bright spot

zone.

Fig 2: Instantaneous amplitude attribute along (a) inline 228 and

(b) horizon H1 showing the bright spot

For the estimation of thickness of the zone, the spectral

decomposition analysis is performed along the inline 228

at different point of intersections with seven cross lines.

Since the thickness of the layer is inversely proportional

to the dominant frequency, the spectral analysis helps to

obtain the dominant frequency in the reservoir zone. Fig-

3 shows the spectral decomposition (amplitude vs

frequency plot) performed at seven points in the reservoir

zone which are intersection points of inline 228 and cross

lines 1010,1016,1022,1028,1034,1040 and 1046.

Fig 3: Spectral decomposition (FFT) plot of amplitude vs

frequency calculated at different points of intersection of inline

228 and cross lines: 1010, 1016, 1022, 1028, 1034,1040 and

1046.

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Fig-3 shows the maximum amplitude occurring at 40-45

Hz which is the dominant frequency in the reservoir zone.

A P wave velocity (v) of about 2200 m/s is assumed

because of non-availability of the well derived velocity

information in the reservoir zone. From the relation of

frequency (f) with velocity (v) and wavelength () we

have:

Hence, the tuning thickness is

Thus from the above analysis the thickness of the

reservoir zone is approximately 12.22 m.

It is well known that a shadow frequency zone below a

gas reservoir is common; therefore a spectral

decomposition analysis is performed below the reservoir

zone as well to detect this shadow zone. Fig-4 shows the

result from the spectral decomposition. The dominant

frequency in this case is between 25-30 Hz. This

indicates lowering-off the frequencies due to the presence

of gas pocket in there servoir zone, confirming the

shadow frequency zone.

Fig 4: Spectral decomposition analysis immediate below the

reservoir zone for different frequencies at different points of

intersection of inline 228 and cross lines

(1010,1016,1022,1028,1034,1040 and 1046).

Fig 5: Crossplots of porosity and (a) instantaneous amplitude

(b) quality factor.

The porosity is estimated from the crossplot analysis

between instantaneous amplitude attribute and the

porosity log derived from the well location. The porosity

log data gives the porosity at the well location to be 25%

- 35%.

From the crossplots shown in Fig-5, a linear regression fit

between porosity data and the seismic attribute is

estimated. This porosity obtained from the regression

relation is extrapolated throughout the reservoir. Fig-6

shows the distribution of porosity in the reservoir zone.

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Fig 6: Distribution of porosity (a) using relation established

between porosity and instantaneous amplitude, (b) using

relation between porosity and quality factor.

Conclusions and Discussions

In this study the seismic attributes computed from

seismic sections and analyses of well log data were

utilized to characterize the reservoir. Both the qualitative

as well as the quantitative characterization of the

reservoir were done. The qualitative study includes the

identification of the bright spot indicating the presence of

biogenic gas packet in the reservoir zone. However to

confirm this gas pocket the quantitative analysis was

done and the thickness of the reservoir zone was

estimated to be 12.22 m which was within the seismic

resolution limit. The spatial variation of porosity was also

estimated along the horizon H1 passing through the

reservoir using the relationship established between

seismic attribute (instantaneous amplitude) and the

porosity derived from well log. The porosity value in the

reservoir zone varies between 28% - 33 % suggesting a

good porous reservoir. This result can be further

improved with the availability of data from more wells in

the F3 block near the reservoir zone and with the use of

stochastic inversion and neural network to enhance the

predicted value of porosity.

Acknowledgement

I extend my sincere gratitude towards my supervisor Dr.

K. Hemant Singhand timely support from Dr. C.H. Mehta

who immensely helped me in completing the project.

dGBEarth Sciences B.V.is kindly acknowledged for

providing the Opendtect software and the OpendTect

Share Seismic Data repository for downloading the

seismic and well log data.

References

Partyka, G., 2001, Seismic Thickness Estimation: Three

approaches pros and cons: SEG International Exposition

and Annual Meeting San Antonio, Texas.

Partyka, G., Gridley, J., and Lopez, J., 1999,

Interpretational applications of spectral decomposition in

reservoircharacterization: The Leading Edge, Vol. 18(3),

pp. 353-360.

Taner, M T, Koehler, F, and Sheriff, R E, 1979,

Complex seismic trace analysis: Geophysics, Vol 44, pp.

1041-1063

Liu J., and Marfurt K. J.,2006, Thin bed thickness

prediction using peak instantaneous frequency: SEG/New

Orleans annual meeting, pp. 968-972.